The emergence of the quadruple play offering, a service bundle that includes data, voice, video and wireless services in the broadband network, along with the emergence of parasitic applications such as Peer to Peer networks (P2P), has contributed to the rapid increase in the traffic levels and network congestion on the cable network.
The majority of the bandwidth on the cable network is often consumed by a very small percentage of users. For example, only 3% of the users may contribute to 47% of the upstream traffic and 58% of all the downstream traffic. These subscribers can cause significant revenue loss as well as deteriorate the network performance for normal users. Providing more bandwidth may solve the problem temporarily, but will increase the infrastructural and operational costs.
Heavy users not only consume a disproportionate amount of bandwidth, they also cause the responsiveness of the network to decrease for the other users. For example, the network's MRT (Mean Response Time), which is analogous to the user's perception of network responsiveness, may increase greatly when the interface utilization of a CMTS interface goes above 60%. Users may observe twice-slower than normal responsiveness at 59% of the interface utilization and ten times slower than normal responsiveness when interface utilization reaches around 71%. The usage band between 59% and 71% is a very sensitive band where the perceived slowness of the network by the user is exponentially increasing. This would make the user feel that the network is congested.
The heavy users that are causing this congestion form a small percentage of the total number of subscribers on the network. When the network is congested, the heavy user's traffic usage needs to be examined, reprioritized and capped in such a way that the other normal users do not perceive the slowness of the network and at the same time heavy users are allowed as much bandwidth as possible without deteriorating the performance of the network for other users.
Even though the percentage of heavy users is small, it is not a constant. Finding out the appropriate number of heavy users to prioritize for capping and redistribution of bandwidth and also how much of their bandwidth should be capped and redistributed are important considerations. It is desirable is to allow fair use of the bandwidth across all users while impacting the least number of heavy users with the smallest amount of bandwidth limitation.